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    "info": {
        "author": "M. Farrajota",
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            "Development Status :: 3 - Alpha",
            "Environment :: MacOS X",
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            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: MIT License",
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            "Programming Language :: Python",
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        "description": ".. note::\n   For the latest source, discussion, etc, please visit the\n   `GitHub repository <https://github.com/dbcollection/dbcollection>`_\n\n\ndbcollection\n============\n\n|Join the chat at https://gitter.im/dbcollection/dbcollection|\n\n|Build Status| |CircleCI| |Build status| |codecov| |License: MIT|\n|Documentation Status| |PyPI version|\n\n**dbcollection** is a library for downloading/parsing/managing datasets via simple methods.\nIt was built from the ground up to be cross-platform (**Windows**, **Linux**, **MacOS**) and\ncross-language (**Python**, **Lua**, **Matlab**, etc.). This is achieved by using the popular ``HDF5``\nfile format to store (meta)data of manually parsed datasets and the power of Python for\nscripting. By doing so, this library can target any platform that supports Python and\nany language that has bindings for ``HDF5``.\n\nThis package allows to easily manage and load datasets by using ``HDF5`` files to store\nmetadata. By storing all the necessary metadata to disk, managing either big or small\ndatasets has an equal or very similar impact on the system's resource usage.\nAlso, once a dataset is setup, it is setup forever! This means users can reuse any\npreviously set dataset as many times as needed without having to set it each time they\nare used.\n\n**dbcollection** allows users to focus on more important tasks like prototyping new models\nor testing them in different datasets without having to incur the loss of spending time managing\ndatasets or creating/modyfing scripts to load/fetch data by taking advantage\nof the work of the community that shared these resources.\n\nMain features\n-------------\n\nHere are some of key features dbcollection provides:\n\n- Simple API to load/download/setup/manage datasets.\n- Simple API to fetch data from a dataset.\n- Store and pull data from disk or from memory, you choose!\n- Datasets only need to be set/processed once, so next time you use it it will load instantly!\n- Cross-platform (**Windows**, **Linux**, **MacOs**).\n- Cross-language (**Python**, **Lua/Torch7**, **Matlab**).\n- Easily extensible to other languages that support ``HDF5`` files format.\n- Concurrent/parallel data access thanks to ``HDF5``.\n- Contains a diverse (and growing!) list of popular datasets for machine-, deep-learning tasks\n  (*object detection*, *action recognition*, *human pose estimation*, etc.)\n\nSupported languages\n-------------------\n\n-  Python (>=2.7 or >=3.5)\n-  Lua/Torch7 (`link`_)\n-  Matlab (>=2014a)\n   (`link <https://github.com/dbcollection/dbcollection-matlab>`__)\n\nPackage installation\n--------------------\n\nFrom PyPi\n~~~~~~~~~\n\nInstalling ``dbcollection`` using pip is simple. For that purpose,\nsimply do the following command:\n\n::\n\n    $ pip install dbcollection\n\nFrom source\n~~~~~~~~~~~\n\nTo install **dbcollection** from source you need to do the following\nsetps:\n\n-  Clone the repo to your hard drive:\n\n::\n\n    $ git clone --recursive https://github.com/dbcollection/dbcollection\n\n-  ``cd`` to the dbcollection folder and do the command\n\n::\n\n    $ python setup.py install\n\nGetting started\n---------------\n\nBasic usage\n~~~~~~~~~~~\n\nUsing the module is pretty straight-forward. To import it just do:\n\n.. code:: python\n\n    >>> import dbcollection as dbc\n\nTo load a dataset, you only need to use a single method that returns a\ndata loader object which can then be used to fetch data from.\n\n.. code:: python\n\n    >>> mnist = dbc.load('mnist')\n\nThis data loader object contains information about the dataset\u2019s name,\ntask, data, cache paths, set splits, and some methods for querying and\nloading data from the ``HDF5`` metadata file.\n\nFor example, if you want to know how the data is structured inside the\nmetadata file, you can simply do the following:\n\n.. code:: python\n\n    >>> mnist.info()\n\n    > Set: test\n       - classes,        shape = (10, 2),          dtype = uint8\n       - images,         shape = (10000, 28, 28),  dtype = uint8,  (in 'object_ids', position = 0)\n       - labels,         shape = (10000,),         dtype = uint8,  (in 'object_ids', position = 1)\n       - object_fields,  shape = (2, 7),           dtype = uint8\n       - object_ids,     shape = (10000, 2),       dtype = uint8\n\n       (Pre-ordered lists)\n       - list_images_per_class,  shape = (10, 1135),  dtype = int32\n\n    > Set: train\n       - classes,        shape = (10, 2),          dtype = uint8\n       - images,         shape = (60000, 28, 28),  dtype = uint8,  (in 'object_ids', position = 0)\n       - labels,         shape = (60000,),         dtype = uint8,  (in 'object_ids', position = 1)\n       - object_fields,  shape = (2, 7),           dtype = uint8\n       - object_ids,     shape = (60000, 2),       dtype = uint8\n\n       (Pre-ordered lists)\n       - list_images_per_class,  shape = (10, 6742),  dtype = int32\n\nTo fetch data samples from a field, its is as easy as calling a method\nwith the set and field names and the row id(s) you want to select. For\nexample, to retrieve the 10 first images all you need to do is the\nfollowing:\n\n.. code:: python\n\n    >>> imgs = mnist.get('train', 'images', range(10))\n    >>> imgs.shape\n    (10, 28, 28)\n\n..\n\n    Note: For more information about using this module, please check the\n    documentation or the available notebooks for guidance.\n\nNotebooks\n~~~~~~~~~\n\nFor a more pratical introduction to **dbcollection\u2019s** module for\nmanaging datasets and fetching data, there are some python notebooks\navailable in the ``notebooks/`` folder for a more hands-on tutorial on\nhow to use this package.\n\nDocumentation\n-------------\n\nThe package documentation is hosted on `Read The Docs`_.\n\nIt provides a more detailed guide on how to use this package as well as\nadditional information that you might find relevant about this project.\n\nContributing\n------------\n\nAll contributions, bug reports, bug fixes, documentation improvements,\nenhancements and ideas are welcome. If you would like to see additional\nlanguages being supported, please consider contributing to the project.\n\nIf you are interested in fixing issues and contributing directly to the\ncode base, please see the document `How to Contribute`_.\n\nFeedback\n--------\n\nFor now, use the `Github issues`_ for requests/bug issues or use our `Gitter room`_\nfor any other questions you may have.\n\nLicense\n-------\n\n`MIT License`_\n\n.. _link: https://github.com/dbcollection/dbcollection-torch7\n\n.. |Join the chat at https://gitter.im/dbcollection/dbcollection| image:: https://badges.gitter.im/dbcollection/dbcollection.svg\n   :target: https://gitter.im/dbcollection/dbcollection?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge\n.. |Build Status| image:: https://travis-ci.org/dbcollection/dbcollection.svg?branch=master\n   :target: https://travis-ci.org/dbcollection/dbcollection\n.. |CircleCI| image:: https://circleci.com/gh/dbcollection/dbcollection/tree/master.svg?style=svg\n   :target: https://circleci.com/gh/dbcollection/dbcollection/tree/master\n.. |Build status| image:: https://ci.appveyor.com/api/projects/status/85gpibosxhjo8yjl/branch/master?svg=true\n   :target: https://ci.appveyor.com/project/farrajota/dbcollection-x3l0d/branch/master\n.. |codecov| image:: https://codecov.io/gh/dbcollection/dbcollection/branch/master/graph/badge.svg\n   :target: https://codecov.io/gh/dbcollection/dbcollection\n.. |License: MIT| image:: https://img.shields.io/badge/License-MIT-yellow.svg\n   :target: https://opensource.org/licenses/MIT\n.. |Documentation Status| image:: https://readthedocs.org/projects/dbcollection/badge/?version=latest\n   :target: http://dbcollection.readthedocs.io/en/latest/?badge=latest\n.. |PyPI version| image:: https://badge.fury.io/py/dbcollection.svg\n   :target: https://badge.fury.io/py/dbcollection\n\n.. _Read The Docs: http://dbcollection.readthedocs.io/en/latest/\n.. _How to Contribute: https://github.com/dbcollection/dbcollection/blob/master/docs/source/contributing/how_to_contribute.rst\n.. _Github issues: https://github.com/dbcollection/dbcollection/issues\n.. _Gitter room: https://gitter.im/dbcollection/dbcollection\n.. _MIT License: LICENSE.txt\n",
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